code: def get_embedding_operators_config() -> OperatorConfig:
      weight_quantization_spec = QuantizationSpec(
          dtype=torch.uint8,
          qscheme=torch.per_channel_affine_float_qparams,
          ch_axis=0,
          observer_or_fake_quant_ctr=PerChannelMinMaxObserver.with_args(
              eps=2**-12
          ),
      )
      quantization_config = QuantizationConfig(
          None, None, weight_quantization_spec, None
      )
      ops: List[OperatorPatternType] = [[torch.nn.Embedding]]
      ops.append([F.embedding])
      supported_config_and_operators = OperatorConfig(
          config=quantization_config, operators=ops
      )
      return copy.deepcopy(supported_config_and_operators)
  
file_path: pytorch/torch/ao/quantization/quantizer/embedding_quantizer.py
imports: from __future__ import annotations
  import copy
  from typing import List, Set
  import torch
  import torch.nn.functional
  from torch.ao.quantization.observer import PerChannelMinMaxObserver
  from torch.ao.quantization.quantizer.quantizer import QuantizationAnnotation, QuantizationSpec, Quantizer
  from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import OperatorConfig, OperatorPatternType, QuantizationConfig
label_desc: Use image node_id 1 for calling a global function with example usage: get_embedding_operators_config() and returns: copy